Crane multi-source sensing fusion working condition safety system and control method

Abstract

The invention discloses a crane multi-source sensing fusion working condition safety system, which comprises a multi-source sensing unit used for collecting crane state and environment multi-source data in real time; the decision-making unit is used for quantizing dynamic risk output of the multi-source data, obtaining an optimal working condition parameter by combining the hoisting utilization rate and the risk value, and outputting a grading early warning result and a control decision based on the risk value and the hoisting utilization rate; and the execution unit displays the output result of the decision unit and limits the driving control of the crane based on the control decision. According to the method, a dynamic working condition adaptation mechanism is constructed by fusing multi-source environmental interference factors, so that full-process closed-loop safety control from hoisting planning, parameter matching to monitoring execution is realized, major accidents such as overload and overturning are actively prevented, and optimal adaptive working condition parameters are actively searched when the hoisting utilization rate is low.

Description

Technical Field

[0001] This invention relates to the field of crane operation safety, and in particular to a crane operation safety system and control method based on multi-source sensing fusion. Background Technology

[0002] Modern engineering construction is trending towards large-scale and intensive operations, often taking place in space-constrained areas. This presents crane operations with multiple challenges: confined spaces, widespread lifting of oversized components, and multiple coupled external disturbances such as sudden winds and uneven foundation settlement. Meanwhile, existing technologies have significant shortcomings: operating parameter settings rely on manual experience, resulting in low matching accuracy, low equipment operating efficiency, and high safety risks; safety warnings are based on single sensor data, lacking multi-source fusion analysis and comprehensive risk assessment; and there is a lack of a comprehensive assessment mechanism for lifting utilization and risk values, making it difficult to balance safety and efficiency. Summary of the Invention

[0003] Purpose of the invention: In order to overcome the shortcomings of the prior art, the first objective of this invention is to provide a working condition safety system for cranes based on multi-source perception fusion. The second objective is to provide a working condition safety control method for cranes based on the multi-source perception fusion of the aforementioned working condition safety system.

[0004] Technical solution: The crane multi-source sensing fusion working condition safety system provided by this invention includes: Multi-source sensing unit, used to collect multi-source data on crane status and environment in real time; The decision-making unit quantifies and dynamically outputs risks from multiple sources, combines hoisting utilization rate and risk value to obtain optimal operating condition parameters, and outputs graded early warning results and control decisions based on risk value and hoisting utilization rate. The execution unit displays the output of the decision-making unit and limits the crane's drive control based on the control decisions.

[0005] Furthermore, the multi-source sensing unit includes: A load sensor, installed on the axis of the hook pulley block, is used to measure the lifting load in real time; The boom angle sensor is installed at the hinge point between the boom and the turntable to detect the boom elevation angle. Outrigger pressure sensors are installed in the piston chamber of the crane's outrigger cylinders to monitor the grounding pressure of each outrigger. A wire rope tension sensor is installed at the fixed end of the wire rope in the hoisting mechanism to dynamically measure the safety margin of the wire rope. Binocular cameras are installed on both sides of the boom root to build a point cloud of obstacles in the work area in real time and calculate the distance of potential obstacles; A laser rangefinder sensor is installed in the center of the outrigger support plate. It emits a laser to the ground and measures the change in distance between the outrigger and the ground to obtain the outrigger settlement rate in real time. An ultrasonic anemometer, mounted on an omnidirectional bracket on the top of the cab, is used for layered monitoring of wind direction and speed.

[0006] Furthermore, the decision-making unit is an on-board controller that receives multi-source data via a CAN bus.

[0007] Furthermore, the execution unit includes a display and an execution control module; The display shows operating parameters, hoisting utilization rate, real-time risk value, a list of alternative operating conditions in case of mismatch, a one-click switching function, and displays graded early warning results; the execution control module drives mechanical and electrical equipment to allow operators to operate within the safety boundary under compliant operating conditions, and interlocks unsafe operations and recommends re-selected operating parameters in case of high risk.

[0008] Correspondingly, a crane multi-source sensing fusion-based working condition safety control method is also provided, which is implemented based on the above-mentioned crane multi-source sensing fusion-based working condition safety system, including the following steps: S1. Select risk factors that affect operational safety, including wind speed deviation, obstacle distance deviation, outrigger settlement rate deviation, outrigger pressure imbalance, wire rope tension deviation, and load torque deviation. S2. Quantifying risk value based on a weighted bias model of multi-source data fusion; S3. Calculate the hoisting utilization rate and combine the risk value with the hoisting utilization rate to obtain the working condition safety margin. S4. The decision-making unit obtains the condition adaptability judgment result based on the risk value, hoisting utilization rate and working condition safety margin, and performs status display and control output.

[0009] Furthermore, the wind speed deviation is the deviation between the actual wind speed and the safe wind speed threshold; the obstacle distance deviation is the deviation between the actual obstacle distance and the safe distance threshold; the outrigger settlement rate deviation is the deviation between the actual outrigger settlement rate in the working area and the equipment requirement; the outrigger pressure imbalance is the maximum deviation ratio between the pressure of each outrigger and the average pressure; the wire rope tension deviation is the deviation between the actual tension and the rated safe tension; and the load torque deviation is the deviation between the load torque and the rated torque given by the current working condition parameters.

[0010] Furthermore, S2 standardizes the deviation of risk factors and, combined with weight allocation, ultimately outputs a quantitative risk value ranging from 0% to 100%. ; In the formula: Risk The quantified risk value; Δ iLet ε be the deviation of the i-th risk factor. i Let the weight of the i-th risk factor satisfy: .

[0011] Furthermore, in S3, the hoisting utilization rate under the current working condition is calculated based on the actual hoisting load and the rated load in the performance table given for the current working condition: ; In the formula: This refers to the actual weight lifted. The rated lifting capacity under the current operating conditions; The safety margins for each operating condition parameter, considering both environmental risk and hoisting utilization rate, are as follows: ; In the formula: S i For the first i Safety margin for each operating condition; For the impact of environmental risks on the safety margin, satisfy 0 ≤ e ≤1, where β As a risk attenuation factor, Risk Environmental risk level, Risk The larger the value, the smaller the safety margin. or >1 or S i ＜α α is an adjustable safety margin threshold. If 1 ≤ α ≤ 2, the current operating condition is determined to be a mismatch condition. The operating condition search engine is automatically invoked to iteratively optimize parameter combinations within the equipment performance table, and outputs the condition that meets the requirements. x ≤ or < c And the optimal operating parameters corresponding to the maximum safety margin, x and c Let be the minimum and maximum thresholds for hoisting utilization, respectively, satisfying 0 ≤ 0. x ≤1, 0≤ c ≤1.

[0012] Furthermore, a dual-threshold graded response mechanism is established. When the comprehensive risk value exceeds the first-level threshold, a first-level audible and visual warning is activated on the display, and a speed limit command is sent to the actuator through the controller to reduce the speed of dangerous actions to 50% of the rated value. When the comprehensive risk value exceeds the second-level threshold, non-optimal operating conditions are forcibly restricted, and a more suitable alternative operating condition is automatically recommended on the display. When the hoisting utilization rate is lower than the set threshold, the efficiency optimization module is triggered, generating an efficiency warning icon and an efficient operating condition alternative on the display, and supporting one-click switching of operating condition configuration.

[0013] Beneficial effects: Compared with the prior art, the significant advantages of this invention are: 1. By integrating multiple environmental interference factors such as wind speed, geology, and obstacles, a dynamic working condition adaptation mechanism is constructed to achieve closed-loop safety management of the entire process from hoisting planning and parameter matching to execution monitoring, proactively preventing major accidents such as overload and overturning, and actively searching for the optimal adaptable working condition parameters when the hoisting utilization rate is low; 2. The proposed hierarchical early warning decision-making function makes full use of the existing hardware resources of the crane. The perception layer reuses the original vehicle CAN bus sensor network (such as pressure / tilt sensors), and the decision-making layer is deployed on the vehicle controller, resulting in a lower system cost. 3. The hoisting utilization rate and environmental risk quantification mechanism can realize the calculation of safety risks, the early warning of resource waste, and the optimization of operating parameters, thereby achieving a balance between risk avoidance and hoisting efficiency; 4. Multi-level early warning effectively improves response speed to hazardous conditions such as strong winds and soft soil. Real-time recommendations of high-utilization alternatives effectively reduce the rate of misoperation. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the system connection of the present invention; Figure 2 This is a schematic diagram of the arrangement of the multi-source sensing units of the present invention; Figure 3 This is a schematic diagram of the control decision output of the present invention; Figure 4 This is the system control logic diagram of the present invention. Detailed Implementation

[0015] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0016] like Figure 1 The crane multi-source sensing fusion working condition safety system shown includes: Multi-source sensing units are used to collect multi-source data on crane status and environment in real time, such as... Figure 2 As shown, the multi-source sensing unit includes: a load sensor 1, installed on the hook pulley block axis, for real-time measurement of the lifting load; a boom angle sensor 2, installed at the hinge point between the boom and the turntable, for detecting the boom elevation angle; a outrigger pressure sensor 3, installed in the piston chamber of the crane outrigger cylinder, for monitoring the ground pressure of each outrigger; a wire rope tension sensor 4, installed at the fixed end of the wire rope of the hoisting mechanism, for dynamically measuring the safety margin of the wire rope; a binocular camera 5, installed on both sides of the boom root, for real-time construction of obstacle point clouds in the working area and calculation of the distance to potential obstacles; a laser rangefinder 6, installed in the center of the outrigger support plate, for emitting lasers towards the ground and measuring the change in distance between the outrigger and the ground to obtain the outrigger settlement rate in real time; and an ultrasonic anemometer 7, installed on the omnidirectional bracket on the top of the cab, for layered monitoring of wind direction and wind speed.

[0017] The decision unit is an intelligent computing unit that realizes "data fusion, working condition adaptation, and risk management". It is mainly used for dynamic working condition adaptability determination, working condition search engine, risk quantification, and control decision output. The decision unit is an on-board controller that receives multi-source data through the CAN bus, quantifies the multi-source data and outputs dynamic risks, combines the hoisting utilization rate and risk value to obtain the optimal working condition parameters, and outputs graded early warning results and control decisions based on the risk value and hoisting utilization rate.

[0018] The execution unit displays the output of the decision-making unit and restricts the crane's drive control based on the control decisions. The execution unit includes a display and an execution control module. The display shows operating parameters, lifting utilization rate, real-time risk value, a list of alternative operating conditions in case of mismatch, a one-click switching function, and displays graded early warning results. The execution control module drives mechanical and electrical equipment to allow operators to operate within the safety boundary under compliant operating conditions, interlocks unsafe operations in high-risk situations, and recommends re-selected operating parameters.

[0019] like Figure 3 and 4 The crane multi-source sensing fusion working condition safety control method shown is implemented based on the above-mentioned crane multi-source sensing fusion working condition safety system, and includes the following steps: S1. Six core parameters that significantly impact crane operation safety are selected as risk factors, covering three dimensions: environment, equipment, and operation. These include wind speed deviation, obstacle distance deviation, outrigger settlement rate deviation, outrigger pressure imbalance, wire rope tension deviation, and load torque deviation. Wind speed deviation is the deviation between the actual wind speed and the safe wind speed threshold; obstacle distance deviation is the deviation between the actual obstacle distance and the safe distance threshold; outrigger settlement rate deviation is the deviation between the actual outrigger settlement rate in the operating area and the equipment requirements; outrigger pressure imbalance is the maximum deviation ratio between the pressure of each outrigger and the average pressure; wire rope tension deviation is the deviation between the actual tension and the rated safe tension; and load torque deviation is the deviation between the load torque and the rated torque given by the current operating parameters.

[0020] S2. Quantifying risk value based on a weighted bias model of multi-source data fusion; The deviation of risk factors is standardized and weighted to ultimately output a quantitative risk value ranging from 0% to 100%. ; In the formula: Risk The quantified risk value; Δ i Let ε be the deviation of the i-th risk factor. i Let the weight of the i-th risk factor satisfy: .

[0021] S3. Calculate the hoisting utilization rate and combine the risk value with the hoisting utilization rate to obtain the working condition safety margin. Based on the actual lifting load and the rated load in the performance table given for the current working condition, calculate the lifting utilization rate under the current working condition: ; In the formula: This refers to the actual weight lifted. The rated lifting capacity under the current operating conditions; The safety margins for each operating condition parameter, considering both environmental risk and hoisting utilization rate, are as follows: ; In the formula: S i For the first i Safety margin for each operating condition; For the impact of environmental risks on the safety margin, satisfy 0 ≤ e ≤1, where β As a risk attenuation factor, Risk Environmental risk level, Risk The larger the value, the smaller the safety margin. or >1 or S i ＜α α is an adjustable safety margin threshold. If 1 ≤ α ≤ 2, the current operating condition is determined to be a mismatch condition. The operating condition search engine is automatically invoked to iteratively optimize parameter combinations within the equipment performance table, and outputs the condition that meets the requirements. x ≤ or < c And the optimal operating parameters corresponding to the maximum safety margin, x and c Let be the minimum and maximum thresholds for hoisting utilization, respectively, satisfying 0 ≤ 0. x ≤1, 0≤ c ≤1.

[0022] S4. During the execution of lifting parameters, the multi-source sensor network first collects data and inputs it into the intelligent decision-making unit. Based on the calculated risk value, lifting utilization rate, and working condition safety margin, the unit outputs the working condition adaptability judgment result and establishes a dual-threshold hierarchical response mechanism. When the comprehensive risk value exceeds the first-level threshold, a first-level audible and visual warning is activated on the display, and a speed limit command is sent to the actuator through the controller to reduce the speed of dangerous actions to 50% of the rated value. When the comprehensive risk value exceeds the second-level threshold, non-optimal working condition operation is forcibly restricted, and a more suitable alternative working condition solution is automatically recommended on the display. When the lifting utilization rate is lower than the set threshold, the efficiency optimization module is triggered, generating an efficiency warning icon and an efficient working condition alternative solution on the display, and supporting one-click switching of working condition configuration.

[0023] Specifically, the control outputs and status displays are shown in Table 1.

[0024] Table 1 shows examples of status and execution results.

[0025] The specific control logic during the hoisting process is as follows: Figure 4 As shown.

Claims

1. A crane multi-source sensing fusion working condition safety system, characterized in that, include: Multi-source sensing unit, used to collect multi-source data on crane status and environment in real time; The decision-making unit quantifies and dynamically outputs risks from multiple sources, combines hoisting utilization rate and risk value to obtain optimal operating condition parameters, and outputs graded early warning results and control decisions based on risk value and hoisting utilization rate. The execution unit displays the output of the decision-making unit and limits the crane's drive control based on the control decisions.

2. The crane multi-source sensing fusion working condition safety system according to claim 1, characterized in that, The multi-source sensing unit includes: A load sensor (1) is installed on the shaft of the hook pulley block for real-time measurement of the lifting load; A boom angle sensor (2) is installed at the hinge point between the boom and the turntable to detect the boom elevation angle; Outrigger pressure sensor (3) is installed in the piston chamber of the outrigger cylinder of the crane to monitor the ground pressure of each outrigger. A wire rope tension sensor (4) is installed at the fixed end of the wire rope of the hoisting mechanism to dynamically measure the safety margin of the wire rope. A binocular camera (5) is installed on both sides of the boom root to build a point cloud of obstacles in the work area in real time and calculate the distance of potential obstacles; A laser rangefinder (6) is installed in the center of the outrigger support plate and emits a laser to the ground to measure the change in distance between the outrigger and the ground in order to obtain the outrigger settlement rate in real time. An ultrasonic anemometer (7) is installed on an omnidirectional bracket on the top of the cab for layered monitoring of wind direction and wind speed.

3. The crane multi-source sensing fusion working condition safety system according to claim 1, characterized in that, The decision-making unit is an on-board controller that receives multi-source data via a CAN bus.

4. The crane multi-source sensing fusion working condition safety system according to claim 1, characterized in that, The execution unit includes a display and an execution control module; The display shows operating parameters, hoisting utilization rate, real-time risk value, a list of alternative operating conditions in case of mismatch, a one-click switching function, and displays graded early warning results; The execution control module drives mechanical and electrical equipment to allow operators to operate within safe boundaries under compliant operating conditions. In high-risk situations, it interlocks unsafe operations and recommends re-selected operating parameters.

5. A method for safety control of cranes based on multi-source sensing fusion, characterized in that, The crane multi-source perception fusion working condition safety system based on any one of claims 1-4 includes the following steps: S1. Select risk factors that affect operational safety, including wind speed deviation, obstacle distance deviation, outrigger settlement rate deviation, outrigger pressure imbalance, wire rope tension deviation, and load torque deviation. S2. Quantifying risk value based on a weighted bias model of multi-source data fusion; S3. Calculate the hoisting utilization rate and combine the risk value with the hoisting utilization rate to obtain the working condition safety margin. S4. The decision-making unit obtains the condition adaptability judgment result based on the risk value, hoisting utilization rate and working condition safety margin, and performs status display and control output.

6. The crane multi-source sensing fusion working condition safety control method according to claim 5, characterized in that: The wind speed deviation is the deviation between the actual wind speed and the safe wind speed threshold; the obstacle distance deviation is the deviation between the actual obstacle distance and the safe distance threshold; the outrigger settlement rate deviation is the deviation between the actual outrigger settlement rate in the working area and the equipment requirement; the outrigger pressure imbalance is the maximum deviation ratio between the pressure of each outrigger and the average pressure; the wire rope tension deviation is the deviation between the actual tension and the rated safe tension; and the load torque deviation is the deviation between the load torque and the rated torque given by the current working condition parameters.

7. The crane multi-source sensing fusion working condition safety control method according to claim 5, characterized in that: S2 standardizes the deviation of risk factors and, combined with weight allocation, ultimately outputs a quantitative risk value ranging from 0% to 100%. ； In the formula: Risk The quantified risk value; Δ i Let ε be the deviation of the i-th risk factor. i Let the weight of the i-th risk factor satisfy: 。 8. The crane multi-source sensing fusion working condition safety control method according to claim 7, characterized in that: S3 uses the actual lifting load and the rated load in the performance table given for the current working condition as benchmarks to calculate the lifting utilization rate under the current working condition: ； In the formula: This refers to the actual weight lifted. The rated lifting capacity under the current operating conditions; The safety margins for each operating condition parameter, considering both environmental risk and hoisting utilization rate, are as follows: ； In the formula: S i For the first i Safety margin for each operating condition; For the impact of environmental risks on the safety margin, satisfy 0 ≤ e ≤1, where β As a risk attenuation factor, Risk Environmental risk level, Risk The larger the value, the smaller the safety margin. η >1 or S i ＜α α is an adjustable safety margin threshold. If 1 ≤ α ≤ 2, the current operating condition is determined to be a mismatch condition. The operating condition search engine is automatically invoked to iteratively optimize parameter combinations within the equipment performance table, and outputs the condition that meets the requirements. ξ ≤ η < γ And the optimal operating parameters corresponding to the maximum safety margin, ξ and γ Let be the minimum and maximum thresholds for hoisting utilization, respectively, satisfying 0 ≤ 0. ξ ≤1, 0≤ γ ≤1.

9. The crane multi-source sensing fusion working condition safety control method according to claim 8, characterized in that: A dual-threshold graded response mechanism is established. When the comprehensive risk value exceeds the first-level threshold, a first-level audible and visual warning is activated on the display, and a speed limit command is sent to the actuator through the controller to reduce the speed of dangerous actions to 50% of the rated value. When the comprehensive risk value exceeds the second-level threshold, non-optimal operating conditions are forcibly restricted, and a more suitable alternative operating condition is automatically recommended on the display. When the hoisting utilization rate is lower than the set threshold, the efficiency optimization module is triggered, generating an efficiency warning icon and an efficient operating condition alternative on the display, and supporting one-click switching of operating condition configuration.

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